[scikit-learn] merging the predicted labels with original dataframe

[Ruchika Nayyar]

The original dataset contains both trainng/testing, I have predictions only
on testing dataset. If I do what you suggest
will it preserve indexing?

Thanks,
Ruchika


On Thu, Jul 20, 2017 at 11:37 AM, Julio Antonio Soto de Vicente <
julio at esbet.es> wrote:

> Hi Ruchika,
>
> The predictions outputted by all sklearn models are just 1-d Numpy arrays,
> so it should be trivial to add it to any existing DataFrame:
>
> your_df["prediction"] = clf.predict(X_test)
>
> --
> Julio
>
> El 20 jul 2017, a las 17:23, Ruchika Nayyar <ruchika.work at gmail.com>
> escribió:
>
> Hi Scikit-learn Users,
>
> I am analyzing some proxy logs to use Machine learning to classify the
> events recorded as either "OBSERVED" or "BLOCKED". This is a little snippet
> of my code:
> The input file is a csv with tokenized string fields.
>
> **************
> # load the file
> M = pd.read_csv("output100k.csv").fillna('')
>
> # define the fields to use
> min_df = 0.001
> max_df = .7
> TxtCols = ['request__tokens', 'requestClientApplication__tokens',
>            'destinationZoneURI__tokens','cs-categories__tokens',
>            'fileType__tokens', 'requestMethod__tokens','tcp_status1',
>            'app','tcp_status2','dhost'
>           ]
> NumCols = ['rt', 'out', 'in', 'time-taken','rt_length', 'dt_length']
>
> # vectorize the fields
> TfidfModels = [TfidfVectorizer(min_df = min_df, max_df=max_df).fit(M[t])
> for t in TxtCols]
>
> # define the columns of sparse matrix
> X = hstack([m.transform(M[n].fillna('')) for m,n in zip(TfidfModels,
> TxtCols)] + \
>                [csr_matrix(pd.to_numeric(M[n]).fillna(-1).values).T for n
> in NumCols])
>
> # target variable
> Y = M.act.values
>
> ## Define train/test parts and scale them
> X_train, X_test, y_train, y_test = tts(X, Y, test_size=0.2)
> scaler = StandardScaler(with_mean=False, with_std=True)
> scaler.fit(X_train)
> X_train=scaler.transform(X_train)
> X_test=scaler.transform(X_test)
>
>
> # define the model and train
> clf = MLPClassifier(activation='logistic', solver='lbfgs').fit(X_train,y_
> train)
> # use the model to predict on X_test and convert into a data frame
> df=pd.DataFrame(clf.predict(X_test))
>
> **
>
> 199845  OBSERVED
> 199846  OBSERVED
>
> [199847 rows x 1 columns]>
>
> **
>
> Now at the end I have a DataFrame with 20K entries with just one column
> "Label", how di I connect it to the main dataframe M, since I want to do
> some
> investigations on this outcome ?
>
> Any help?
>
> Thanks,
> Ruchika
>
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